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Record W3012383649 · doi:10.1038/s41545-020-0053-y

Comparing utility-reported hours of piped water supply to households’ experiences

2020· article· en· W3012383649 on OpenAlexaff
Farah Rawas, Robert Bain, Emily Kumpel

Bibliographic record

Venuenpj Clean Water · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of British Columbia
FundersUniversity of Massachusetts Amherst
KeywordsWater supplyPopulationBenchmarkingWater utilityBusinessWater resource managementGeographyEnvironmental scienceEnvironmental engineeringEnvironmental healthMarketing

Abstract

fetched live from OpenAlex

Abstract Piped water supplies that deliver water only intermittently serve more than one billion people around the world. Measuring the continuity of these intermittent water supplies (IWS) is necessary for tracking utility performance and understanding the impact on consumers. We compared reporting of IWS continuity between utility benchmarking data on hours of water supply and households’ reports of water outages in piped systems in Peru. Nationally, the estimated population with IWS as reported by households (23–26%) was slightly higher than that reported by utilities (20–21%) from 2010 to 2014. While the national population with IWS was markedly similar between both measures, household- and utility-reported IWS matched in only 2 of the 23 regions of Peru. We examined the influence of varying thresholds of water supply continuity on the results. Our findings highlight a need to reconcile different approaches to monitoring piped water supply continuity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.279
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2020
Admission routes1
Has abstractyes

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